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2.3.1 Types of Big Data Analytics

i. Diagnostic Analytics

According to Riahi (2018), diagnostic analytics is the process of
analysing prior data to determine the root cause of a specific event or

problem. It assists in comprehending what took place and what can

be done to stop it from occurring again. For instance, diagnostic
analytics can be used to determine why sales of a given product have

decreased.

Diagnostic analytics utilizes different techniques, including data
exploration, data mining, correlation analysis, and regression

analysis, to uncover patterns and relationships within the data. By

identifying the key drivers or contributing factors, researcher can gain
a deeper understanding of the problem and make informed decisions

on how to address it.

ii. Descriptive Analytics

In descriptive analytics, prior data is examined to determine what
occurred in a specific event or circumstance (Dunmade, 2022).

Insights into trends and patterns can be gained by summarising and

visualising data. Descriptive analytics, for instance, can be used to
comprehend the sales trends of a specific product over time.

Descriptive analytics utilizes various techniques such as data

aggregation, data visualization, statistical analysis, and exploratory

data analysis to present a comprehensive view of the data. Through
charts, graphs, tables, and summary statistics, descriptive analytics

helps stakeholders understand the past performance, trends, and
characteristics of a particular phenomenon.

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